Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations....

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Main Authors: Napi N.N.L.M., Abdullah S., Mansor A.A., Ghazali N.A., Ahmed A.N., Dom N.C., Ismail M.
Other Authors: 57702029500
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Published: Penerbit UTHM 2024
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spelling my.uniten.dspace-347022024-10-14T11:21:52Z Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area Napi N.N.L.M. Abdullah S. Mansor A.A. Ghazali N.A. Ahmed A.N. Dom N.C. Ismail M. 57702029500 56509029800 57211858557 26430938300 57214837520 57217286875 57210403363 gaseous pollutant industrial meteorological multiple linear regression Ozone Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations. Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3 concentration were developed. MLR3 had the highest correlation coefficient of 0.792 during development as compared to models MLR1 and MLR2. MLR2 was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models. The establishment of an O3 prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions. � 2023 UTHM Publisher. All rights reserved. Final 2024-10-14T03:21:52Z 2024-10-14T03:21:52Z 2023 Article 10.30880/ijie.2023.15.01.010 2-s2.0-85152670270 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152670270&doi=10.30880%2fijie.2023.15.01.010&partnerID=40&md5=c8852861b94efa67c188866ac16469e3 https://irepository.uniten.edu.my/handle/123456789/34702 15 1 106 117 All Open Access Bronze Open Access Green Open Access Penerbit UTHM Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic gaseous pollutant
industrial
meteorological
multiple linear regression
Ozone
spellingShingle gaseous pollutant
industrial
meteorological
multiple linear regression
Ozone
Napi N.N.L.M.
Abdullah S.
Mansor A.A.
Ghazali N.A.
Ahmed A.N.
Dom N.C.
Ismail M.
Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
description Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations. Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3 concentration were developed. MLR3 had the highest correlation coefficient of 0.792 during development as compared to models MLR1 and MLR2. MLR2 was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models. The establishment of an O3 prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions. � 2023 UTHM Publisher. All rights reserved.
author2 57702029500
author_facet 57702029500
Napi N.N.L.M.
Abdullah S.
Mansor A.A.
Ghazali N.A.
Ahmed A.N.
Dom N.C.
Ismail M.
format Article
author Napi N.N.L.M.
Abdullah S.
Mansor A.A.
Ghazali N.A.
Ahmed A.N.
Dom N.C.
Ismail M.
author_sort Napi N.N.L.M.
title Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
title_short Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
title_full Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
title_fullStr Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
title_full_unstemmed Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
title_sort different approaches of multiple linear regression (mlr) model in predicting ozone (o3) concentration in industrial area
publisher Penerbit UTHM
publishDate 2024
_version_ 1814061191725580288
score 13.209306